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<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>Replication files for Bucchianeri, et al 2021, &quot;What
explains local policy cleavages? Examining the policy preferences of public
officials at the municipal level&quot;, Social Science Quarterly.� </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>The files in the directory use the software R to create all
Tables and Figures in article and Appendix, except for Figure A1, which was
made using ArcGIS</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>Directory prepared by Ryan D. Enos, January 5, 2021.</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=ES>Contact: Ryan Enos, renos@gov.harvard.edu</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'>####</p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>&nbsp;</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>Files:<br>
&quot;ReplicationFinal.R&quot; - the master replication script. Executing the
script will create the tables and figures and output them to the directory.�
Script was created using R 4.1.0.</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>&quot;coeff_plot_functions.R&quot; - functions for creating
plots.� This is called by &quot;ReplicationFinal.R&quot;.� No action is
necessary for using this script. </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>&quot;MayorsCityData.csv&quot; - data from survey of mayors
and city councilors combined with Census Data on localities for the survey
subjects.� Variables are described below.</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>&quot;IPUMSData.csv&quot; - data from 2015 American Community
Survey: 5-Year Data at Place level for all places in US (excluding Puerto Rico)
with population of 20 or more.� Variables are described below. </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>������������� </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>&nbsp;</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>Variables in &quot;MayorsCityData.csv&quot;:</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>1. Resp_ID: numeric respondent ID<br>
2. tradeoff.ineq, tradeoff.propval, tradeoff.climate, tradeoff.housing,
tradeoff.charters, tradeoff.minwage, tradeoff.police, tradeoff.transit,
tradeoff.lgbt, tradeoff.private, tradeoff.schools, tradeoff.justice: tradeoff
questions described in Appendix B, coded 1-5 �Strongly disagree� to �Strongly
agree� with 1 representing Strongly disagree and 5 representing Strongly agree.<br>
3. tradeoffs_d1: Respondent Ideal Point on 1st dimension of policy space scaled
from tradeoff questions.� See paper for details.� <br>
4. tradeoffs_d2: Respondent Ideal Point on 2nd dimension of policy space scaled
from tradeoff questions.� See paper for details.� <br>
5. partyID: 3 point party ID converted from 5 point party ID with independent leaners
converted to Democrat or Republican using information from party2 variable, 1 =
Democrat, 2 = Republican, 3 = Independent<br>
6. party: 3 point party ID with independent leaners NOT converted to Democrat
or Republican, 1 = Democrat, 2 = Republican, 3 = Independent<br>
7. party2: to which party do independent respondents in &quot;party&quot; question
lean.� Missing for respondents not indicating independent in &quot;party&quot;
question<br>
8. type: mayor or councilor<br>
9. race: self-reported race, including Hispanic<br>
10. gender: self-reported gender<br>
11. ideology: self-reported ideology, numeric scaled 1-5 from &quot;very
liberal&quot; to &quot;very conservative&quot;<br>
12. progressive: whether respondent considers themselves to be a progressive,
categorical with five points from &quot;definitely yes&quot; to
&quot;definitely no&quot;<br>
13. dem: 1 for self-reported Democrat and Democrat leaners, 0 otherwise<br>
14. white: 1 if self-reported race is white, 0 otherwise<br>
15. mayor: 1 if mayor, 0 otherwise<br>
16. pnwhite: locality percent white (including Hispanic) from 2015 ACS<br>
17. phis : locality percent Hispanic or Latino (of any race) from 2015 ACS<br>
18. logpop: locality log total population from 2015 ACS<br>
19. log_debt_out_per_cap: locality log public debt per capita from 2012 Census
of Governments<br>
20. pcol: percent of locality 25 years or older with bachelors degree or higher
from 2015 ACS<br>
21. support variables: responses to question to �rank the strength of support
that you have received� from a set of common local interest groups on scale
from 1-5, coarsened to 1 = Support, 0 = Neither Support nor Oppose, -1 =
Oppose. support.a_c = &quot;Local Business Groups&quot;, support.b_c =
&quot;Faith-Based Orgs.&quot;, support.c_c = &quot;Civic/Fraternal Orgs.&quot;,
support.d_c = &quot;Environmental Groups&quot;, support.e_c = &quot;Health
Industry&quot;, support.f_c = &quot;Large Individual Donors&quot;, support.g_c
= &quot;LGBT Orgs.&quot;, support.h_c = &quot;Local Democratic Comm.&quot;,
support.i_c = &quot;Local Republican Comm.&quot;, support.j_c = 'Newspapers',
support.k_c = &quot;Police/Firefighter Unions&quot;, support.l_c =
&quot;Teacher's Unions&quot;, support.m_c = &quot;Other Unions&quot;,
support.n_c = &quot;State Officials&quot;, support.o_c = &quot;Federal
Officials&quot;, support.p_c = &quot;Real Estate Industry&quot;, support.q_c =
&quot;Women's Orgs.&quot;<br>
22. city_name: place name <br>
23. pop: locality total population from 2015 ACS<br>
24. pmin: locality percent not &quot;white alone&quot; from 2015 ACS<br>
25. Inc: locality Per capita income in the past 12 months from 2015 ACS<br>
26. logInc: locality log Per capita income in the past 12 months from 2015 ACS<br>
27. pman: locality percent of population 16 years or older employed in
manufacturing from 2015 ACS���� </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>��������������������� </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>Variables in &quot;IPUMSData.csv&quot;:</span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>1. STATE: state<br>
2. city_name: place name<br>
3. pop: see above<br>
4. logpop: see above<br>
5. pmin: see above<br>
6. phis: see above<br>
7. Inc: see above<br>
8. logInc: see above<br>
9. pcol: see above<br>
10. pman: see above </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>�������������� </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>���������������������� </span></p>

<p class=MsoNormal style='margin-bottom:10.0pt;line-height:115%;text-autospace:
none'><span lang=EN>���������������������� </span></p>

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